Model predictive method and system for controlling and supervising insulin infusion

ABSTRACT

A system and method for controlling and monitoring a diabetes-management system through the use of a model that predicts or estimates future dynamic states of glucose and insulin from variables such as insulin delivery or exogenous glucose appearance as well as inherent physiological parameters. The model predictive estimator can be used as an insulin bolus advisor to give an apriori estimate of postprandial glucose for a given insulin delivery profile administered with a known meal to optimize insulin delivery; as a supervisor to monitor the operation of the diabetes-management system; and as a model predictive controller to optimize the automated delivery of insulin into a user&#39;s body to achieve a desired blood glucose profile or concentration. Open loop, closed-loop, and semi-closed loop embodiments of the invention utilize a mathematical metabolic model that includes a Minimal Model, a Pump Delivery to Plasma Insulin Model, and a Meal Appearance Rate Model.

RELATED APPLICATION DATA

This is a divisional of patent application Ser. No. 11/700,666, filedJan. 31, 2007, which is incorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates generally to model predictive control andsupervision, and more specifically, to improved methods and apparatusesthat apply model predictive strategies to the operation and monitoringof diabetes-management systems.

2. Description of Related Art

The pancreas of a normal healthy person produces and releases insulininto the blood stream in response to elevated blood plasma glucoselevels. Beta cells (β-cells), which reside in the pancreas, produce andsecrete the insulin into the blood stream, as it is needed. If β-cellsbecome incapacitated or die, a condition known as Type I diabetesmellitus (or in some cases if β-cells produce insufficient quantities ofinsulin, Type II diabetes), then insulin must be provided to the bodyfrom another source.

Traditionally, since insulin cannot be taken orally, insulin has beeninjected with a syringe. More recently, the use of infusion pump therapyhas been increasing, especially for delivering insulin for diabetics.For example, external infusion pumps are worn on a belt, in a pocket, orthe like, and deliver insulin into the body via an infusion tube with apercutaneous needle or a cannula placed in the subcutaneous tissue. Asof 1995, less than 5% of Type I diabetics in the United States wereusing infusion pump therapy. More recently, over 7% of the more than900,000 Type I diabetics in the U.S. are using infusion pump therapy.Also, the percentage of Type I diabetics who use an infusion pump isgrowing at an absolute rate of over 2% each year. Moreover, the numberof Type I diabetics is growing at 3% or more per year. In addition,growing numbers of insulin-using Type II diabetics are also usinginfusion pumps. Physicians have recognized that continuous infusionprovides greater control of a diabetic's condition, and are alsoincreasingly prescribing it for patients.

Infusion pump devices and systems are relatively well-known in themedical arts for use in delivering or dispensing a prescribedmedication, such as insulin, to a patient. In one form, such devicescomprise a relatively compact pump housing adapted to receive a syringeor reservoir carrying a prescribed medication for administration to thepatient through infusion tubing and an associated catheter or infusionset. Programmable controls can operate the infusion pump continuously orat periodic intervals to obtain a closely controlled and accuratedelivery of the medication over an extended period of time. Suchinfusion pumps are used to administer insulin and other medications,with exemplary pump constructions being shown and described in U.S. Pat.Nos. 4,562,751; 4,678,408; 4,685,903; 5,080,653; and 5,097,122, whichare incorporated by reference herein.

There is a baseline insulin need for each body which, in diabeticindividuals, may generally be maintained by administration of a basalamount of insulin to the patient on a continual, or continuous, basisusing infusion pumps. However, when additional glucose (i.e., beyond thebasal level) appears in a diabetic individual's body, such as, forexample, when the individual consumes a meal, the amount and timing ofthe insulin to be administered must be determined so as to adequatelyaccount for the additional glucose while, at the same time, avoidinginfusion of too much insulin. Typically, a bolus amount of insulin isadministered to compensate for meals (i.e., meal bolus). It is commonfor diabetics to determine the amount of insulin that they may need tocover an anticipated meal based on carbohydrate content of the meal.

Although the administration of basal and bolus amounts of insulin froman infusion pump provides a diabetic individual reasonable control ofhis/her blood glucose levels, there still exists a need to betterprovide control for the administration of insulin to more closelyresemble the body's insulin response, and to avoid overdoses of insulin.

SUMMARY OF THE PREFERRED EMBODIMENTS

Improved methods and systems are provided that apply model predictivestrategies to the operation and monitoring of diabetes-managementsystems. Embodiments of the invention are directed to a system andmethod for using model predictive bolus estimation for optimizingdelivery of insulin into the body to cover a meal of certaincarbohydrate amount and type. Using this information and the currentglucose and insulin state, an apriori glucose profile is predicted for auser defined insulin bolus based on the model. If the glucose profile isacceptable, the user can confirm the insulin bolus, which would then bedelivered by the pump. If the predicted profile is unacceptable, theuser can modify the bolus amount or type until an acceptable predictedglucose profile is obtained. Alternatively, the system can derive andsuggest an insulin delivery strategy that would yield a predictedpostprandial glucose profile according to predefined guidelines.

In a further embodiment in connection with a diabetes-management systemhaving a glucose sensor and an insulin pump, a supervisory model is usedto predict the dynamical state of the system, namely, the currentestimate of glucose concentration given a history of meals and pastinsulin delivery profile. At each point in time, the state estimate ofglucose concentration is compared with the measured sensor glucosevalue. If the difference between the measured glucose and estimatedglucose exceeds a pre-determined error value, the system can alert theuser that the discrepancy may be attributed to a failing, or failed,glucose sensor or insulin catheter.

In yet a further embodiment of the invention, a model predictivecontroller is used for automated closed loop continuous insulin deliveryusing known historical insulin delivery information, meal information,and glucose state. The model predictive controller optimizes an insulindelivery profile over a predetermined control horizon (time period) inthe future in order to achieve a predicted estimated future glucoseprofile that is closest to a desired glucose profile. At each time step,insulin is delivered at the current calculated insulin delivery rate andthe process is repeated. Here, model predictive control is used tooptimize a future glucose profile over a prediction horizon rather thanjust considering the current and previous state. The model is used toestimate a future predicted glucose profile for each insulin deliveryprofile.

BRIEF DESCRIPTION OF THE DRAWINGS

A detailed description of embodiments of the invention will be made withreference to the accompanying drawings, wherein like numerals designatecorresponding parts in the several figures.

FIG. 1 is a block diagram of a closed loop glucose control system inaccordance with an embodiment of the present invention.

FIG. 2 is a front view of closed loop hardware located on a body inaccordance with an embodiment of the present invention.

FIG. 3( a) is a perspective view of a glucose sensor system for use inan embodiment of the present invention.

FIG. 3( b) is a side cross-sectional view of the glucose sensor systemof FIG. 3( a).

FIG. 3( c) is a perspective view of a sensor set of the glucose sensorsystem of FIG. 3( a) for use in an embodiment of the present invention.

FIG. 3( d) is a side cross-sectional view of the sensor set of FIG. 3(c).

FIG. 4 is a cross sectional view of a sensing end of the sensor of FIG.3( d).

FIG. 5 is a top view of an infusion device with a reservoir door in theopen position, for use in an embodiment of the present invention.

FIG. 6 is a side view of an infusion set with the insertion needlepulled out, for use in an embodiment of the present invention.

FIG. 7 is a circuit diagram of a sensor and its power supply inaccordance with an embodiment of the present invention.

FIG. 8( a) is a diagram of a single device and its components inaccordance with an embodiment of the present invention.

FIG. 8( b) is a diagram of two devices and their components inaccordance with an embodiment of the present invention.

FIG. 8( c) is another diagram of two devices and their components inaccordance with an embodiment of the present invention.

FIG. 8( d) is a diagram of three devices and their components inaccordance with an embodiment of the present invention.

FIG. 9 is a table listing the devices of FIGS. 8 a-8 d and theircomponents.

FIG. 10 illustrates an algorithm for optimizing delivery of insulin intoa body of a user to cover a planned meal in accordance with anembodiment of the present invention.

FIG. 11 shows meal appearance curves for three different meal types inaccordance with an embodiment of the present invention.

FIG. 12( a) shows plasma insulin concentration curves for threedifferent delivery patterns in accordance with an embodiment of thepresent invention.

FIG. 12( b) shows glucose concentration profiles for a fast meal inaccordance with an embodiment of the present invention.

FIG. 12( c) shows glucose concentration profiles for a medium meal inaccordance with an embodiment of the present invention.

FIG. 12( d) shows glucose concentration profiles for a slow meal inaccordance with an embodiment of the present invention.

FIG. 13( a) shows a second set of plasma insulin concentration curvesfor three different delivery patterns in accordance with an embodimentof the present invention.

FIG. 13( b) shows a second set of glucose concentration profiles for afast meal in accordance with an embodiment of the present invention.

FIG. 13( c) shows a second set of glucose concentration profiles for amedium meal in accordance with an embodiment of the present invention.

FIG. 13( d) shows a second set of glucose concentration profiles for aslow meal in accordance with an embodiment of the present invention.

FIG. 14 illustrates an algorithm for monitoring the operation of adiabetes-management system in accordance with an embodiment of thepresent invention.

FIG. 15( a) shows historical insulin delivery and consumed meal dataaccording to an embodiment of the present invention.

FIG. 15( b) shows predicted and sensor glucose profiles in accordancewith an embodiment of the present invention.

FIG. 16 illustrates an algorithm for optimizing the delivery of insulininto the body of a user for achieving a desired blood glucoseconcentration according to an embodiment of the present invention.

FIG. 17 shows estimated insulin delivery, predicted glucoseconcentration, and desired glucose concentration profiles in accordancewith an embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

In the following description, reference is made to the accompanyingdrawings which form a part hereof and which illustrate severalembodiments of the present inventions. It is understood that otherembodiments may be utilized and structural and operational changes maybe made without departing from the scope of the present inventions.

It is also noted that the present invention is described below withreference to flowchart illustrations of methods, apparatus, and/orcomputer program products. It will be understood that each block of theflowchart illustrations, and combinations of blocks in the flowchartillustrations, can be implemented by computer program instructions.These computer program instructions may be loaded onto a computer orother programmable data processing apparatus (including, e.g., thecontroller 12), such that the instructions which execute on the computeror other programmable data processing apparatus create instructions forimplementing the functions specified in the flowchart block or blocks.These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instructions whichimplement the function specified in the flowchart block or blocks. Thecomputer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer implemented process such that theinstructions which execute on the computer or other programmableapparatus provide steps for implementing the functions specified in theflowchart block or blocks.

As shown in the drawings for purposes of illustration, the invention maybe embodied in a closed loop or semi-closed loop diabetes-managementsystem having a sensor, a controller, and an infusion system forregulating the rate of fluid infusion into a body of a user based onuser-specific input variables and feedback from an analyte concentrationmeasurement taken from the user's body. In particular embodiments, theinvention is embodied in a control system for regulating the rate ofinsulin infusion into the body of a user based on a glucoseconcentration measurement taken from the body. In preferred embodiments,the system is designed to model a pancreatic beta cell (β-cell). Inother words, the system controls an infusion device to release insulininto a body of a user in a similar concentration profile as would becreated by fully functioning human β-cells when responding to changes inblood glucose concentrations in the body.

Thus, the system simulates the body's natural insulin response to bloodglucose levels and not only makes efficient use of insulin, but alsoaccounts for other bodily functions as well since insulin has bothmetabolic and mitogenic effects. However, the algorithms must model theβ-cells closely, since algorithms that are designed to minimize glucoseexcursions in the body, without regard for how much insulin isdelivered, may cause excessive weight gain, hypertension, andatherosclerosis. Similarly, algorithms that are designed to minimizeglucose excursions in the body without regard for the time period overwhich the glucose excursion may occur, or the time period over which theinsulin is delivered arid/or the insulin-delivery pattern, may lead tohypoglycemia, thereby causing dizziness, or, in severe cases, death.

In preferred embodiments of the present invention, the system isintended to emulate the in vivo insulin secretion pattern and to adjustthis pattern consistent with the in vivo β-cell adaptation experiencedby normal healthy individuals. The in vivo β-cell response in subjectswith normal glucose tolerance (NGT), with widely varying insulinsensitivity (S_(I)), is the optimal insulin response for the maintenanceof glucose homeostasis.

For example, one way to neutralize glucose appearance due to consumptionof a meal is to deliver insulin in a combination mode, where the amountof insulin to be delivered is divided (e.g., 50%-50%) between a bolusand an extended basal amount over a period of time. Nevertheless, theform of insulin delivery that is most favored by diabetics, even amongstthose who use pumps, is that of a single bolus of short-acting insulin,which is achieved by either injection or an infusion pump of the typediscussed above. In general, this tendency to favor the single-bolusapproach of delivery may be attributed to the average diabetic patient'slimited understanding of how various bolus types, taken with variousmeals, affect their post-prandial glucose profiles.

However, it is known that the carbohydrates of different foods manifestthemselves as glucose in the blood plasma at different rates. As such,it would be advantageous to account to some degree for this rate ofappearance, rather than simply the total amount of carbohydrates. Putanother way, a slice of pizza, a bowl of salad, and a glass of orangejuice may contain the same amount of total carbohydrates (e.g., 50grams) However, whereas the carbohydrates of the glass of orange juicemay appear in the plasma within 15 minutes of consumption (i.e., where15 minutes represents the peak of the appearance rate), thecarbohydrates from consuming the slice of pizza may take upwards of180-200 minutes to appear. Therefore, treating both meals simply byadministering a single bolus that covers for a 50-carbohydrate meal,while effective, would provide a less-than-optimal response.

Similarly, in deciding whether to administer additional insulin (e.g., acorrective bolus), diabetic individuals—or in a closed-loop system, thecontroller—normally consider insulin on board, which may be defined asthe total amount of insulin existing within the body. Insulin on board,however, is less useful as a strategic tool than the plasma insulinconcentration because, by definition, for any individual, his/herinsulin on board is always decreasing. However, at any given point intime after administration of a bolus, the concentration of insulin inthe plasma may be either increasing or decreasing.

And knowing whether the insulin concentration is increasing ordecreasing would allow the patient to make a more informed decisionabout if, when, and how much additional insulin is needed. For example,without this information, a patient who has recently administered abolus of insulin, upon learning that his glucose level is still high,may be apt to administer an additional bolus. However, if a patientknows that his insulin concentration is increasing, he may realize thatthe initial insulin has not yet taken its full effect, and that heshould wait before administering an additional bolus. Thus, whileinsulin on board is informative, it does not convey as much informationas insulin concentration. To this end, a plasma insulin profile wouldprovide to the patient not only the amount of insulin on board, but alsoan indication as to whether the patient's insulin concentration isrising or falling.

In the same vein, it is generally known that the in vivo β-cell responseto changes in glucose is characterized by “first” and “second” phaseinsulin responses. Thus far, this biphasic insulin response has beenmodeled using components of a proportional, plus integral, plusderivative (PID) controller. Depending on the application, a PIDcontroller may prove advantageous since PID algorithms are generallyknown to be stable for a wide variety of non-medical dynamic systems,and PID algorithms have been found to be stable over widely varyingdisturbances and changes in system dynamics.

Nevertheless, existing diabetes-management systems, such as those thatemploy PID controllers, consider historical data and the current stateof the body, only. As such, they do not adequately account for delays inthe insulin-delivery/glucose concentration process. This is significantbecause, for a user who has recently consumed a meal, and has also had abolus of insulin administered to cover the meal, the PID controller ofan existing system that is continually monitoring the user's glucoseconcentration level may find a relatively high glucose level even afterthe bolus has been delivered. The PID controller may then determine thatan additional bolus should be administered. In this way, a PIDcontroller may suggest several consecutive infusions, withoutconsidering the long-range impact (e.g., hypoglycemia) of thecombination of all of the delivered boluses once they have collectivelytaken their maximum effect in the user's body. There is therefore a needfor a diabetes-management system, with an adaptive controller, thatenables optimization of insulin delivery into the body of a user while,at the same time, allowing continuous monitoring of the operation of thediabetes-management system.

In light of the above-noted need, a preferred closed-loop embodiment ofthe invention is practiced with a glucose sensor system 10, a controller12, and an insulin delivery system 14, as shown in FIG. 1. The glucosesensor system 10 generates a sensor signal 16 representative of bloodglucose levels 18 in the body 20, and provides the sensor signal 16 tothe controller 12. The controller 12 receives the sensor signal 16 andgenerates commands 22 that are communicated to the insulin deliverysystem 14. The insulin delivery system 14 receives the commands 22 andinfuses insulin 24 into the body 20 in response to the commands 22.

Generally, the glucose sensor system 10 includes a glucose sensor,sensor electrical components to provide power to the sensor and generatethe sensor signal 16, a sensor communication system to carry the sensorsignal 16 to the controller 12, and a sensor system housing for theelectrical components and the sensor communication system.

Typically, the controller 12 includes controller electrical componentsand software to generate commands for the insulin delivery system 14based on the sensor signal 16, and a controller communication system toreceive the sensor signal 16 and carry commands to the insulin deliverysystem 14.

Generally, the insulin delivery system 14 includes an infusion deviceand an infusion tube to infuse insulin 24 into the body 20. Inparticular embodiments, the infusion device includes infusion electricalcomponents to activate an infusion motor according to the commands 22,an infusion communication system to receive the commands 22 from thecontroller 12, and an infusion device housing to hold the infusiondevice.

In preferred embodiments, the controller 12 is housed in the infusiondevice housing and the infusion communication system is an electricaltrace or a wire that carries the commands 22 from the controller 12 tothe infusion device. In alternative embodiments, the controller 12 ishoused in the sensor system housing and the sensor communication systemis an electrical trace or a wire that carries the sensor signal 16 fromthe sensor electrical components to the controller electricalcomponents. In other alternative embodiments, the controller 12 has itsown housing or is included in a supplemental device. In anotheralternative embodiment, the controller is located with the infusiondevice and the sensor system all within one housing. In furtheralternative embodiments, the sensor, controller, and/or infusioncommunication systems may utilize a cable, a wire, fiber optic lines,RF, IR, or ultrasonic transmitters and receivers, or the like instead ofthe electrical traces. In yet other alternative embodiments, one or moreof the components and/or housing units mentioned above may include adisplay for displaying, e.g., sensor readings, insulin-deliveryrates/patterns, glucose concentration profiles, insulin concentrationprofiles, audio/visual/sensory warnings to the user, etc.

In a semi-closed loop embodiment, the sensor signal 16 may be provideddirectly to the user, who then determines, based on the blood glucoselevel 18, the amount and timing of insulin delivery to the body, andinstructs the controller 12 and/or the insulin delivery system 14accordingly. Alternatively, in a more preferred semi-closed loopembodiment, the controller 12 makes the above determination based on thesensor signal 16 and generates recommendations that are provideddirectly to the user, who must then expressly confirm them. Once thecontroller 12 receives the user's confirmation, it generates commands 22that are communicated to the insulin delivery system 14 foradministering insulin 24 to the user's body.

In the preferred semi-closed loop embodiment, the controller 12 includescontroller electrical components and software to make calculations andgenerate recommendations for the user based on the sensor signal 16, anda controller communication system to receive the sensor signal 16,generate recommendations to the user, and carry commands to the insulindelivery system 14.

As shown in FIG. 2, in preferred embodiments, the present invention maybe practiced with a diabetes-management system comprising a sensor 26, asensor set 28, a telemetered characteristic monitor 30, a sensor cable32, an infusion device 34, an infusion tube 36, and an infusion set 38,all worn on the body 20 of a user. The telemetered characteristicmonitor 30 includes a monitor housing 31 that supports a printed circuitboard 33, batteries 35, antenna (not shown), and a sensor cableconnector (not shown), as seen in FIGS. 3( a) and 3(b). A sensing end 40of the sensor 26 has exposed electrodes 42 and is inserted through skin46 into a subcutaneous tissue 44 of a user's body 20, as shown in FIGS.3( d) and 4. The electrodes 42 are in contact with interstitial fluid(ISF) that is present throughout the subcutaneous tissue 44. The sensor26 is held in place by the sensor set 28, which is adhesively secured tothe user's skin 46, as shown in FIGS. 3( c) and 3(d). The sensor set 28provides for a connector end 27 of the sensor 26 to connect to a firstend 29 of the sensor cable 32. A second end 37 of the sensor cable 32connects to the monitor housing 31. The batteries 35 included in themonitor housing 31 provide power for the sensor 26 and electricalcomponents 39 on the printed circuit board 33. The electrical components39 sample the sensor signal 16 and store digital sensor values (Dsig) ina memory and then periodically transmit the digital sensor values Dsigfrom the memory to the controller 12, which is included in the infusiondevice.

The controller 12 processes the digital sensor values Dsig and generatescommands 22 for the infusion device 34. Preferably, the infusion device34 responds to the commands 22 and actuates a plunger 48 that forcesinsulin 24 out of a reservoir 50 located inside the infusion device 34,as shown in FIG. 5. In particular embodiments, a connector tip 54 of thereservoir 50 extends through the infusion device housing 52 and a firstend 51 of the infusion tube 36 is attached to the connector tip 54. Asecond end 53 of the infusion tube 36 connects to the infusion set 38.Insulin 24 is forced through the infusion tube 36 into the infusion set38 and into the body 20. The infusion set 38 is adhesively attached tothe user's skin 46, as shown in FIG. 6. As part of the infusion set 38,a cannula 56 extends through the skin 46 and terminates in thesubcutaneous tissue 44 completing fluid communication between thereservoir 50 and the subcutaneous tissue 44 of the user's body 20.

In alternative embodiments, the system components may be combined in asmaller or greater number of devices and/or the functions of each devicemay be allocated differently to suit the needs of the user.

In embodiments of the invention, before it is provided as an input tothe controller 12, the sensor signal 16 is generally subjected to signalconditioning such as pre-filtering, filtering, calibrating, or the like.Components such as a pre-filter, one or more filters, a calibrator andthe controller 12 may be split up or physically located together, andmay be included with a telemetered characteristic monitor transmitter30, the infusion device 34, or a supplemental device. In preferredembodiments, the pre-filter, filters, and the calibrator are included aspart of the telemetered characteristic monitor transmitter 30, and thecontroller 12 is included with the infusion device 34, as shown in FIG.8( b). In alternative embodiments, the pre-filter is included with thetelemetered characteristic monitor transmitter 30 and the filter andcalibrator are included with the controller 12 in the infusion device,as shown in FIG. 8( c). In other alternative embodiments, the pre-filtermay be included with the telemetered characteristic monitor transmitter30, while the filter and calibrator are included in the supplementaldevice 41, and the controller is included in the infusion device, asshown in FIG. 8( d). To illustrate the various embodiments in anotherway, FIG. 9 shows a table of the groupings of components (pre-filter,filters, calibrator, and controller) in various devices (telemeteredcharacteristic monitor transmitter, supplemental device, and infusiondevice) from FIGS. 8( a)-8(d). In other alternative embodiments, asupplemental device contains some (or all) of the components.

In preferred embodiments, the sensor system generates a message thatincludes information based on the sensor signal such as digital sensorvalues, pre-filtered digital sensor values, filtered digital sensorvalues, calibrated digital sensor values, commands, or the like. Themessage may include other types of information as well such as a serialnumber, an ID code, a check value, values for other sensed parameters,diagnostic signals, other signals, or the like. In particularembodiments, the digital sensor values Dsig may be filtered in thetelemetered characteristic monitor transmitter 30, and then the filtereddigital sensor values may be included in the message sent to theinfusion device 34 where the filtered digital sensor values arecalibrated and used in the controller. In other embodiments, the digitalsensor values Dsig may be filtered and calibrated before being sent tothe controller 12 in the infusion device 34. Alternatively, the digitalsensor values Dsig may be filtered, and calibrated and used in thecontroller to generate commands 22 that are then sent from thetelemetered characteristic monitor transmitter 30 to the infusion device34.

In further embodiments, additional optional components, such as apost-calibration filter, a display, a recorder, and a blood glucosemeter may be included in the devices with any of the other components orthey may stand-alone. Generally, if a blood glucose meter is built intoone of the devices, it will be co-located in the device that containsthe calibrator. In alternative embodiments, one or more of thecomponents are not used. Also, as noted, in semi-closed loopembodiments, signals, messages, commands, etc. may be sent to, ordisplayed for, the user, who then completes the loop by activelyproviding instructions to the controller and/or the infusion device.

In preferred embodiments, RF telemetry is used to communicate betweendevices, such as the telemetered characteristic monitor transmitter 30and the infusion device 34, which contain groups of components. Inalternative embodiments, other communication mediums may be employedbetween devices such as wires, cables, IR signals, laser signals, fiberoptics, ultrasonic signals, or the like.

In preferred embodiments, after filtering, the digital sensor valuesDsig are calibrated with respect to one or more glucose referencevalues. The glucose reference values are entered into the calibrator andcompared to the digital sensor values Dsig. The calibrator applies acalibration algorithm to convert the digital sensor values Dsig, whichare typically in counts into blood glucose values. In particularembodiments, the calibration method is of the type described in U.S.patent application Ser. No. 09/511,580, filed on Feb. 23, 2000, entitled“GLUCOSE MONITOR CALIBRATION METHODS”, which is incorporated byreference herein. In particular embodiments, the calibrator is includedas part of the infusion device 34 and the glucose reference values areentered by the user into the infusion device 34. In other embodiments,the glucose reference values are entered into the telemeteredcharacteristic monitor transmitter 30 and the calibrator calibrates thedigital sensor values Dsig and transmits calibrated digital sensorvalues to the infusion device 34. In further embodiments, the glucosereference values are entered into a supplemental device where thecalibration is executed. In alternative embodiments, a blood glucosemeter is in communication with the infusion device 34, telemeteredcharacteristic monitor transmitter 30, or supplemental device so thatglucose reference values may be transmitted directly into the devicethat the blood glucose meter is in communication with. In additionalalternative embodiments, the blood glucose meter is part of the infusiondevice 34, telemetered characteristic monitor transmitter 30, orsupplemental device such as that shown in U.S. patent application Ser.No. 09/334,996, filed on Jun. 17, 1999, entitled “CHARACTERISTIC MONITORWITH A CHARACTERISTIC METER AND METHOD OF USING THE SAME”, which isincorporated by reference herein.

In preferred embodiments, to obtain blood glucose reference values, oneor more blood samples are extracted from the body 20, and a common,over-the-counter, blood glucose meter is used to measure the bloodplasma glucose concentration of the samples. Then a digital sensor valueDsig is compared to the blood glucose measurement from the meter and amathematical correction is applied to convert the digital sensor valuesDsig to blood glucose values. In alternative embodiments, a solution ofa known glucose concentration is introduced into the subcutaneous tissuesurrounding the sensor 26 by using methods and apparatus such asdescribed in U.S. patent application Ser. No. 09/395,530, filed on Sep.14, 1999, entitled “METHOD AND KIT FOR SUPPLYING A FLUID TO ASUBCUTANEOUS PLACEMENT SITE”, which is incorporated by reference herein,or by using injection, infusion, jet pressure, introduction through alumen, or the like. A digital sensor value Dsig is collected while thesensor 26 is bathed in the solution of known glucose concentration. Amathematical formula such as a factor, an offset, an equation, or thelike, is derived to convert the digital sensor value Dsig to the knownglucose concentration. The mathematical formula is then applied tosubsequent digital sensors values Dsig to obtain blood glucose values.In alternative embodiments, the sensors are calibrated before they areused in the body or do not require calibration at all.

Thus, the sensor system provides the glucose measurements used by thecontroller. The sensor system includes a sensor, a sensor set to holdthe sensor if needed, a telemetered characteristic monitor transmitter,and a cable if needed to carry power and/or the sensor signal betweenthe sensor and the telemetered characteristic monitor transmitter.

In preferred embodiments, the glucose sensor system 10 includes a thinfilm electrochemical sensor such as the type disclosed in U.S. Pat. No.5,391,250, entitled “METHOD OF FABRICATING THIN FILM SENSORS”; U.S.patent application Ser. No. 09/502,204, filed on Feb. 10, 2000, entitled“IMPROVED ANALYTE SENSOR AND METHOD OF MAKING THE SAME”; or othertypical thin film sensors such as described in commonly assigned U.S.Pat. Nos. 5,390,671; 5,482,473; and 5,586,553 which are incorporated byreference herein. See also U.S. Pat. No. 5,299,571.

The glucose sensor system 10 also includes a sensor set 28 to supportthe sensor 26 such as described in U.S. Pat. No. 5,586,553, entitled“TRANSCUTANEOUS SENSOR INSERTION SET” (published as PCT Application WO96/25088); and U.S. Pat. No. 5,954,643, entitled “INSERTION SET FOR ATRANSCUTANEOUS SENSOR” (published as PCT Application WO 98/56293); andU.S. Pat. No. 5,951,521, entitled “A SUBCUTANEOUS IMPLANTABLE SENSOR SETHAVING THE CAPABILITY TO REMOVE OR DELIVER FLUIDS TO AN INSERTION SITE”,which are incorporated by reference herein.

In preferred embodiments, the sensor 26 is inserted through the user'sskin 46 using an insertion needle 58, which is removed and disposed ofonce the sensor is positioned in the subcutaneous tissue 44. Theinsertion needle 58 has a sharpened tip 59 and an open slot 60 to holdthe sensor during insertion into the skin 46, as shown in FIGS. 3( c)and (d) and FIG. 4. Further description of the needle 58 and the sensorset 28 are found in U.S. Pat. No. 5,586,553, entitled “TRANSCUTANEOUSSENSOR INSERTION SET” (published as PCT Application WO 96/25088); andU.S. Pat. No. 5,954,643, entitled “INSERTION SET FOR A TRANSCUTANEOUSSENSOR” (published as PCT Application WO 98/5629), which areincorporated by reference herein.

In preferred embodiments, the sensor 26 has three electrodes 42 that areexposed to the interstitial fluid (ISF) in the subcutaneous tissue 44 asshown in FIGS. 3( d) and 4. A working electrode WRK, a referenceelectrode REF, and a counter electrode CNT are used to form a circuit,as shown in FIG. 7. When an appropriate voltage is supplied across theworking electrode WRK and the reference electrode REF, the ISF providesimpedance (R1 and R2) between the electrodes 42. And an analog currentsignal Isig flows from the working electrode WRK through the body (R1and R2, which sum to Rs) and to the counter electrode CNT. Preferably,the working electrode WRK is plated with platinum black and coated withglucose oxidase (GOX), the reference electrode REF is coated withsilver-silver chloride, and the counter electrode is plated withplatinum black. The voltage at the working electrode WRK is generallyheld to ground, and the voltage at the reference electrode REF issubstantially held at a set voltage Vset. Vset is between 300 and 700mV, and preferably about 535 mV.

The most prominent reaction stimulated by the voltage difference betweenthe electrodes is the reduction of glucose as it first reacts with GOXto generate gluconic acid and hydrogen peroxide (H₂O₂). Then the H₂O₂ isreduced to water (H₂O) and (O⁻) at the surface of the working electrodeWRK. The O⁻ draws a positive charge from the sensor electricalcomponents, thus repelling an electron and causing an electrical currentflow. This results in the analog current signal Isig being proportionalto the concentration of glucose in the ISF that is in contact with thesensor electrodes 42. The analog current signal Isig flows from theworking electrode WRK, to the counter electrode CNT, typically through afilter and back to the low rail of an op-amp 66. An input to the op-amp66 is the set voltage Vset. The output of the op-amp 66 adjusts thecounter voltage Vcnt at the counter electrode CNT as Isig changes withglucose concentration. The voltage at the working electrode WRK isgenerally held to ground, the voltage at the reference electrode REF isgenerally equal to Vset, and the voltage Vcnt at the counter electrodeCNT varies as needed. In alternative embodiments, more than one sensoris used to measure blood glucose. In particular embodiments, redundantsensors are used.

In alternative embodiments, other continuous blood glucose sensors andsensor sets may be used. In particular alternative embodiments, thesensor system is a micro needle analyte sampling device such asdescribed in U.S. patent application Ser. No. 09/460,121, filed on Dec.13, 1999, entitled “INSERTION SET WITH MICROPIERCING MEMBERS AND METHODSOF USING THE SAME”, incorporated by reference herein, or an internalglucose sensor as described in U.S. Pat. Nos. 5,497,772; 5,660,163;5,791,344; and 5,569,186, and/or a glucose sensor that uses florescencesuch as described in U.S. Pat. No. 6,011,984, all of which areincorporated by reference herein.

In other alternative embodiments, the sensor system uses other sensingtechnologies such as described in Patent Cooperation Treaty publicationNo. WO 99/29230, light beams, conductivity, jet sampling, microdialysis, micro-poration, ultra sonic sampling, reverse iontophoresis,or the like. In still other alternative embodiments, only the workingelectrode WRK is located in the subcutaneous tissue and in contact withthe ISF, and the counter CNT and reference REF electrodes are locatedexternal to the body and in contact with the skin. In particularembodiments, the counter electrode CNT and the reference electrode REFare located on the surface of a monitor housing and are held to the skinas part of the telemetered characteristic monitor. In other particularembodiments, the counter electrode CNT and the reference electrode REFare held to the skin using other devices such as running a wire to theelectrodes and taping the electrodes to the skin, incorporating theelectrodes on the underside of a watch touching the skin, or the like.In more alternative embodiments, more than one working electrode WRK isplaced into the subcutaneous tissue for redundancy. In additionalalternative embodiments, a counter electrode is not used, a referenceelectrode REF is located outside of the body in contact with the skin(e.g., on a monitor housing), and one or more working electrodes WRK arelocated in the ISF. In other embodiments, ISF is harvested from the bodyof an individual and flowed over an external sensor that is notimplanted in the body.

In preferred embodiments, the sensor cable 32 is of the type describedin U.S. Patent Application Ser. No. 60/121,656, filed on Feb. 25, 1999,entitled “TEST PLUG AND CABLE FOR A GLUCOSE MONITOR”, which isincorporated by reference herein. In other embodiments, other cables maybe used such as shielded, low noise cables for carrying nA currents,fiber optic cables, or the like. In alternative embodiments, a shortcable may be used or the sensor may be directly connected to a devicewithout the need of a cable.

In preferred embodiments, the telemetered characteristic monitortransmitter 30 is of the type described in U.S. patent application Ser.No. 09/465,715, filed on Dec. 17, 1999, entitled “TELEMETEREDCHARACTERISTIC MONITOR SYSTEM AND METHOD OF USING THE SAME” (publishedas PCT Application WO 00/19887 and entitled, “TELEMETERED CHARACTERISTICMONITOR SYSTEM”), which is incorporated by reference herein, and isconnected to the sensor set 28 as shown in FIGS. 3( a) and (b).

In alternative embodiments, the sensor cable 32 is connected directly tothe infusion device housing, as shown in FIG. 8( a), which eliminatesthe need for a telemetered characteristic monitor transmitter 30. Theinfusion device contains a power supply and electrical components tooperate the sensor 26 and store sensor signal values.

In other alternative embodiments, the telemetered characteristic monitortransmitter includes a receiver to receive updates or requests foradditional sensor data or to receive a confirmation (a hand-shakesignal) indicating that information has been received correctly.Specifically, if the telemetered characteristic monitor transmitter doesnot receive a confirmation signal from the infusion device, then itre-sends the information. In particular alternative embodiments, theinfusion device anticipates receiving blood glucose values or otherinformation on a periodic basis. If the expected information is notsupplied when required, the infusion device sends a ‘wake-up’ signal tothe telemetered characteristic monitor transmitter to cause it tore-send the information.

Once a sensor signal 16 is received and processed through the controller12, commands 22 are generated to operate the infusion device 34. Inpreferred embodiments, semi-automated medication infusion devices of theexternal type are used, as generally described in U.S. Pat. Nos.4,562,751; 4,678,408; 4,685,903; and U.S. patent application Ser. No.09/334,858, filed on Jun. 17, 1999, entitled “EXTERNAL INFUSION DEVICEWITH REMOTE PROGRAMMING, BOLUS ESTIMATOR AND/OR VIBRATION CAPABILITIES”(published as PCT application WO 00/10628), which are hereinincorporated by reference. In alternative embodiments, automatedimplantable medication infusion devices, as generally described in U.S.Pat. Nos. 4,373,527 and 4,573,994, are used, which are incorporated byreference herein.

In preferred embodiments, the infusion device reservoir 50 containsHumalog® lispro insulin to be infused into the body 20. Alternatively,other forms of insulin may be used such as Humalin®, human insulin,bovine insulin, porcine insulin, analogs, or other insulins such asinsulin types described in U.S. Pat. No. 5,807,315, entitled “METHOD ANDCOMPOSITIONS FOR THE DELIVERY OF MONOMERIC PROTEINS”, and U.S. PatentApplication Ser. No. 60/177,897, filed on Jan. 24, 2000, entitled “MIXEDBUFFER SYSTEM FOR STABILIZING POLYPEPTIDE FORMULATIONS”, which areincorporated by reference herein, or the like. In further alternativeembodiments, other components are added to the insulin such aspolypeptides described in U.S. patent application Ser. No. 09/334,676,filed on Jun. 25, 1999, entitled “MULTIPLE AGENT DIABETES THERAPY”,small molecule insulin mimetic materials such as described in U.S.patent application Ser. No. 09/566,877, filed on May 8, 2000, entitled“DEVICE AND METHOD FOR INFUSION OF SMALL MOLECULE INSULIN MIMETICMATERIALS”, both of which are incorporated by reference herein, or thelike.

In preferred embodiments, an infusion tube 36 is used to carry theinsulin 24 from the infusion device 34 to the infusion set 38. Inalternative embodiments, the infusion tube carries the insulin 24 frominfusion device 34 directly into the body 20. In further alternativeembodiments, no infusion tube is needed, for example if the infusiondevice is attached directly to the skin and the insulin 24 flows fromthe infusion device, through a cannula or needle directly into the body.In other alternative embodiments, the infusion device is internal to thebody and an infusion tube may or may not be used to carry insulin awayfrom the infusion device location.

In preferred embodiments, the infusion set 38 is of the type describedin U.S. Pat. No. 4,755,173, entitled “SOFT CANNULA SUBCUTANEOUSINJECTION SET”, which is incorporated by reference herein. Inalternative embodiments, other infusion sets, such as the Rapid set fromDesetronic, the Silhouette from MiniMed, or the like, may be used. Infurther alternative embodiments, no infusion set is required, forexample if the infusion device is an internal infusion device or if theinfusion device is attached directly to the skin.

In further alternative embodiments, the pre-filter, filters, calibratorand/or controller 12 are located in a supplemental device that is incommunication with both the telemetered characteristic monitortransmitter 30 and the infusion device 34. Examples of supplementaldevices include a hand held personal digital assistant such as describedin U.S. patent application Ser. No. 09/487,423, filed on Jan. 20, 2000,entitled “HANDHELD PERSONAL DATA ASSISTANT (PDA) WITH A MEDICAL DEVICEAND METHOD OF USING THE SAME”, which is incorporated by referenceherein, a computer, a module that may be attached to the telemeteredcharacteristic monitor transmitter 30, a module that may be attached tothe infusion device 34, a RF programmer such as described in U.S. patentapplication Ser. No. 09/334,858, filed on Jun. 17, 1999, entitled“EXTERNAL INFUSION DEVICE WITH REMOTE PROGRAMMING, BOLUS ESTIMATORAND/OR VIBRATION CAPABILITIES” (published as PCT application WO00/10628), which is incorporated by reference herein, or the like. Inparticular embodiments, the supplemental device includes apost-calibration filter, a display, a recorder, and/or a blood glucosemeter. In further alternative embodiments, the supplemental deviceincludes a method and means for a user to add or modify information tobe communicated to the infusion device 34 and/or the telemeteredcharacteristic monitor transmitter 30 such as buttons, a keyboard, atouch screen, a voice-recognition device, and the like.

In preferred embodiments, the controller 12 is designed to model apancreatic beta cell (β-cell). In other words, the controller 12commands the infusion device 34 to release insulin 24 into the body 20at a rate that causes the insulin concentration in the blood to follow asimilar concentration profile as would be caused by fully functioninghuman β-cells responding to blood glucose concentrations in the body 20.

A controller that simulates the body's natural insulin response to bloodglucose levels not only makes efficient use of insulin but also accountsfor other bodily functions as well since insulin has both metabolic andmitogenic effects. Controller algorithms that are designed to minimizeglucose excursions in the body without regard for how much insulin isdelivered may cause excessive weight gain, hypertension, andatherosclerosis. Similarly, algorithms that are designed to minimizeglucose excursions in the body without regard for the time period overwhich the glucose excursion may occur, or the time period over which theinsulin is delivered and/or the insulin-delivery pattern, may lead tohypoglycemia, thereby causing dizziness, or, in severe cases, death.Therefore, in preferred embodiments of the present invention, thecontroller 12 is intended to emulate the in vivo insulin secretionpattern and to adjust this pattern to be consistent with in vivo β-celladaptation. The in vivo β-cell response in subjects with normal glucosetolerance (NGT), with widely varying insulin sensitivity (S_(I)), is theoptimal insulin response for the maintenance of glucose homeostasis.

Generally, in a normally glucose tolerant human body, healthy β-cellsbenefit from such inputs as neural stimulation, gut hormone stimulation,changes in free fatty acid (FFA) and protein stimulation, etc. Thus, inpreferred embodiments, the user may manually input into the controller12 supplemental information such as a start of a meal, an anticipatedcarbohydrate content of the meal, a start of a sleep cycle, anticipatedsleep duration, anticipated exercise duration and intensity, or thelike. Then, a model predictive estimator (MPE) feature assists thecontroller to use the supplemental information to anticipate changes inglucose concentration and modify the insulin delivery accordingly. Forexample, in a NGT individual, neural stimulation triggers the β-cells tobegin to secrete insulin into the blood stream before a meal begins,which is well before the blood glucose concentration begins to rise. So,in alternative embodiments, the user can tell the system that a meal isbeginning and the pump will deliver insulin in anticipation of the meal.In a more preferred embodiment, the controller is a Model PredictiveController (MPC) that uses a model of subsequent outcomes to control andsupervise the operation of a diabetes-management system in such a way asto optimize delivery of insulin to a user's body to compensate forβ-cells that perform inadequately.

Thus, embodiments of the present invention are directed to amathematical metabolic model that is used in conjunction with a modelpredictive estimator to replicate insulin and glucose kinetics anddynamics so as to mimic true physiological profiles. These profiles maythen be used to optimize decisions about bolus amounts, insulin-deliverypatterns, timing of insulin delivery, and the overall operationalefficiency of the diabetes-management system. Thus, in a closed-loopsystem, for example, the controller 12 may perform this optimization andprovide appropriate delivery commands 22 to the infusion device. In asemi-closed loop system, on the other hand, the profiles created by thecontroller may be displayed on the infusion device, such that theuser/patient can adopt the best strategy (i.e., glucose profile) andthen instruct the controller or infusion device accordingly.

The mathematical metabolic model according to the preferred embodimentof the invention comprises three sets of equations which relate,respectively, to: (1) a “Minimal Model” that describes the change inglucose concentration (G), and the proportional reduction of glucosedisappearance due to insulin (Y), as a function of time; (2) a “PumpDelivery to Plasma Insulin Model”, which is a two-compartment model thatderives plasma insulin concentrations from pump delivery (therebydescribing the change over time of insulin concentration in the plasma(I_(P)) and of insulin concentration in the remote compartment (I_(R)));and (3) a “Meal Appearance Rate Model”, which is also a two-compartmentmodel (with the same time constant) that describes the rate ofappearance of a meal (R_(A)).

Specifically, each of the Minimal Model, the Pump Delivery to PlasmaInsulin Model, and the Meal Appearance Rate Model may be describedmathematically as follows:

Minimal Model

$\begin{matrix}{{\frac{G}{t}(t)} = {{{- \left( {{GEZI} + {S_{I}*Y}} \right)}*{G(t)}} + p_{4} + {R_{A}(t)}}} & {{Eqn}.\mspace{14mu} (1)} \\{{\frac{Y}{t}(t)} = {p_{2}*\left( {{- {Y(t)}} + {I_{p}(t)}} \right)}} & {{Eqn}.\mspace{14mu} (2)}\end{matrix}$

Pump Delivery to Plasma Insulin Model

$\begin{matrix}{{\frac{I_{R}}{t}(t)} = {\frac{1}{\tau_{1}}*\left( {{- {I_{R}(t)}} + \frac{\frac{R_{B}(t)}{60}}{C_{I}*10^{- 6}}} \right)}} & {{Eqn}.\mspace{14mu} (3)} \\{{\frac{I_{P}}{t}(t)} = {\frac{1}{\tau_{2}}*\left( {{- {I_{P}(t)}} + {I_{R}(t)}} \right)({normalized})}} & {{Eqn}.\mspace{14mu} (4)}\end{matrix}$

Meal Appearance Rate Model

$\begin{matrix}{{R_{A}(t)} = {\frac{C_{H}}{V_{G}*\tau_{m}^{2}}*^{- \frac{t}{\tau_{m}}}}} & {{Eqn}.\mspace{14mu} (5)}\end{matrix}$

Wherein the following variables are defined as:

-   -   Y: Proportional reduction of glucose disappearance due to        insulin (unitless)    -   R_(A): Exogenous meal appearance rate (mg/dl/min)    -   I_(P): Insulin concentration in the plasma (μU/ml)    -   I_(R): Insulin concentration in the remote compartment (μU/ml)    -   C_(l): Insulin clearance (ml/min)        And the following parameters are defined as:    -   GEZI: Glucose effectiveness at zero insulin    -   p₂: Insulin action time constant    -   S_(I): Insulin Sensitivity    -   p₄: Endogenous glucose production    -   τ₁: Insulin time constant out of the remote compartment    -   τ₂: Insulin time constant out of the plasma space    -   V_(G): Glucose distribution volume    -   τ_(m): Time constants for meal out of the first and second        compartments (this is equivalent to the peak time of the meal        appearance rate curve)    -   C_(H): Carbohydrate ingested    -   I_(SB): Insulin given as a single bolus for a meal    -   T_(D): Time duration of extended bolus

Embodiments of the present invention are directed to model predictivetechniques that may utilize the above mathematical metabolic model in atleast three ways: (1) as a model predictive bolus estimator (MPBE) in amethod of optimizing delivery of insulin into a body of a user to covera planned meal (see FIG. 10); (2) as a model predictive supervisor (MPS)in a method of monitoring the operation of a diabetes-management system(see FIG. 14); and (3) as a model predictive controller (MPC) in amethod of optimizing the delivery of insulin into the body of a user soas to achieve a desired blood glucose concentration (see FIG. 16). Theseembodiments—which, in the ensuing discussion, are referred to as the“first”, “second”, and “third” embodiment, respectively—will bedescribed below in conjunction with a specific example. It will beunderstood, however, that the specific example is provided by way ofillustration only, and not limitation.

For the specific illustrative example, the following fit parameters wereused:

GEZI: 3.69 * 10⁻⁴ min⁻¹ Glucose effectiveness at zero insulin p₂: 0.01min⁻¹ Insulin action time constant S₁: 3.23 * 10⁻⁴ ml/μU/min InsulinSensitivity p₄: 0.606 mg/dl/min Endogenous glucose production τ₁: 45.26min Insulin time constant out of the remote compartment τ₂: 45.35 minInsulin time constant out of the plasma space V_(G): 209.46 dl Glucosedistribution volume τ_(m): 15, 60, & 200 min Time constants for meal outof the first and second compartments, where each specific value isequivalent to the peak time of the meal appearance rate curve C_(H):50000 mg Carbohydrate ingested I_(SB): 3U Insulin given as a singlebolus for a meal T_(D): 2 or 4 hrs Time duration of extended bolus

Solving Equations (1)-(5) for the initial conditions, i.e., steady-stateconditions immediately prior to the time of administering a meal bolusat time t=0, yields:

$\begin{matrix}{{G(0)} = \frac{p_{4}}{\left( {{GEZI} + {p_{2}*{Y(0)}}} \right)}} & {{Eqn}.\mspace{14mu} (6)} \\{{Y(0)} = {S_{I}*{I_{P}(0)}}} & {{Eqn}.\mspace{14mu} (7)} \\{{I_{R}(0)} = {\frac{1}{C_{I}*10^{- 6}}*\left( {\frac{R_{B}(0)}{60} + {\frac{1}{\tau_{1}}*{B_{I}(0)}}} \right)}} & {{Eqn}.\mspace{14mu} (8)} \\{{I_{P}(0)} = \frac{\frac{R_{B}(0)}{60}}{C_{I}*10^{- 6}}} & {{Eqn}.\mspace{14mu} (9)}\end{matrix}$

The first embodiment, involving a model predictive bolus estimator willnow be described with reference to FIG. 10. In this embodiment, thebasic inquiry may be summarized as follows: For the same amount ofinsulin delivered to compensate for a meal (i.e., where the areas underthe respective plasma insulin curves are the same), how do thepostprandial glucose (PPG) profiles compare for different distributions,or delivery patterns, of insulin?

With the above in mind, the process of optimizing the delivery ofinsulin into the body of a patient to cover a planned meal thus startswith identifying values for the input parameters at block 100. Thisincludes the fit parameters noted above. Thus, for example, theanticipated meal is assumed to have 50 g of carbohydrates, and the bolusamount (I_(SB)) is taken to be 3 U. In addition, three meal types havebeen identified, wherein a “fast meal” has a time constant τ_(m) of 15minutes, a “medium meal” has a time constant τ_(m) of 60 minutes, and a“slow meal” has a time constant τ_(m) of 200 minutes.

In addition, R_(B)(0) is set to 1.6 U/hr in accordance with the fitparameters in order to start the glucose concentration at 100 mg/dl. Theassumption, for the purposes of this example, is that the patient hasthe correct basal rate prior to the meal, thereby yielding a startingglucose at euglycemia. Moreover, the amount of insulin given to coverthe meal with a carbohydrate content of 50 g was determined by the totalnumber of units necessary for a single bolus to yield a peak PPGconcentration of 180 mg/dl (i.e., the maximum peak PPG per ADAguidelines) for the typical “medium meal” R_(A) (peak at 60 min).

It is noted that, in answering the basic inquiry noted above, variousinsulin delivery patterns may be modeled using the model predictiveestimator. For example, a user may wish to compare the (postprandial,post-infusion) glucose profiles that would result if the 3 U of insulinwere delivered as a single bolus at the time of the meal, as opposed toan “extended bolus”, where a portion of the bolus is delivered at thetime of the meal, and the remainder is delivered unifomily over a periodof time. For the purposes of the specific example used herein, threedelivery patterns, and two extended periods of time, were used. Thedelivery patterns include: (1) a basal delivery, wherein no additionalbolus is administered in view of the user's consumption of the meal; (2)a single-bolus delivery, wherein the entirety of the 3 U of insulin isdelivered at time t=0, i.e., at the time of the meal; and (3) anextended bolus delivery, wherein it was assumed that 50% of the boluswas delivered at time t=0, and the remaining 50% was delivered uniformlyover an extended period of time (and in addition to the meal-independentbasal rate of 1.6 U/hr). It should be understood, however, that forextended bolus delivery, the bolus may be delivered in variousproportions other than 50%-50%. Also, for the illustrative example, thetime duration of the extended bolus (T_(D)) was allowed to be either 2hours or 4 hours. Again, any other time period may also be used.

Referring back to FIG. 10, using the above input parameters with Eqns.(1)-(5), glucose profiles can now be generated at block 110. Morespecifically, Eqn. (5) may first be used to generate meal appearanceprofiles for the various meal types. As noted before, the mealssimulated for this example were: a meal with a fast rate of appearance(e.g., orange juice: 15 min. R_(A) peak); a meal with a medium rate ofappearance (e.g., salad: 60 min. R_(A) peak); and a meal with a slowrate of appearance (e.g., pizza: 200 min. R_(A) peak). Thus, as shown inFIG. 11, the 50 g carbohydrate content 101 for each meal is assumed tohave been consumed at time t=0, with the curve 103 for the fast mealpeaking at about time t=15 min., the curve 105 for the medium mealpeaking at about time t=1 hr., and the curve 107 for the slow mealpeaking at about t=3.3 hrs. As can be seen from the Minimal Model, theexogenous meal appearance rate R_(A) of Eqn. (5) will subsequently beused as an input in Eqn. (1).

Next, Eqns. (3) and (4)—that is, the-Pump Delivery to Plasma InsulinModel—may be used to generate plasma insulin profiles for the variousdelivery patterns. For the example herein, FIG. 12( a) shows the plasmainsulin curves for the basal pattern 109, the single-bolus pattern 111,and the 2-hour extended bolus pattern 113. FIG. 13( a) shows the plasmainsulin curve for the 4-hour extended bolus pattern 115. As notedpreviously, because they provide the user with an indication of whetherplasma insulin concentration is decreasing or increasing, FIGS. 12( a)and 13(a) are more informative for the user than an indication ofinsulin on board alone. As such, the Pump Delivery to Plasma InsulinModel provides the user with a more powerful tool in deciding if, orwhen, additional insulin should be administered.

Next, Eqns. (1) and (2) are used to generate the glucose profiles. Thus,for example, FIG. 12( b) shows the glucose profile for each of a basaldelivery 117, a single-bolus delivery 119, and a 2-hour extendeddelivery 121 for a fast meal. FIGS. 12( c) and 12(d) show the glucoseprofiles for the same three delivery patterns, assuming, respectively, amedium absorbing meal, and a slow absorbing meal. Similarly, FIGS. 13(b)-13(d) show the glucose profiles assuming all three delivery profilesfor each meal type, except that the time duration of the extended bolusis now 4 hours.

Once the glucose profiles for all combinations of delivery pattern andmeal type have been generated, the algorithm then inquires, at block120, whether a specific glucose profile is acceptable. Thus, in asemi-closed loop system, for example, the above-noted glucose profilesmay be displayed on the controller, the pump, etc., and the user may beasked to provide approval of one of the glucose profiles. In theillustrative example, the user might have inquired as to all three mealtypes in order to decide which meal to consume. Having seen theresultant postprandial glucose profiles, the user can now decide whetherany one of them would be acceptable.

If the user finds that none of the generated glucose profiles isacceptable, he may opt to change one or more of the input parameters(block 140), and run the algorithm again. Thus, the user may decide, forexample, that a different bolus amount (I_(SB)) should be used. Or, theuser may wish to input a different “type” of meal, i.e., one having apeak rate that is different from the three that were previously modeled.Similarly, for the extended delivery pattern, the user may wish toconsider different proportions for the immediate vs. extended-deliveryphases of insulin administration. The algorithm can therefore beimplemented iteratively, until an acceptable glucose profile has beenfound. It is noted that, in a closed-loop system, the same logic may beused, where acceptability may be determined by, e.g., comparing thegenerated glucose profiles to a pre-defined profile, such as one that isderived in accordance with ADA guidelines for the given inputparameters.

Once one of the glucose profiles has been approved, insulin isdelivered, at block 130, to the user according to the delivery patternand extended-time duration, if any, of the accepted profile. Thus, uponthe user's indication of approval, and/or upon the controller'sdetermination that one of the glucose profiles is within acceptablerange of a pre-defined profile, the controller 12 generates andtransmits commands 22 to the insulin delivery system 14 to deliverinsulin to the user's body accordingly. See FIG. 1.

The second embodiment noted above, which is directed to a method ofmonitoring the operation of the diabetes-management system using a modelpredictive supervisor, will now be described with reference to FIGS. 14and 15.

The algorithm starts at block 200, wherein historical meal information,including the carbohydrate content and a meal-type indicator for eachmeal consumed by a user is stored. As with the first embodiment, themeal-type indicator may be the “type” of meal, e.g., a fast meal. Inthis case, the estimator will then associate the meal-type with anaverage peak time for the appearance rate of that “type” of meal, e.g.,200 minutes for a fast meal. Alternatively, the meal-type indicator maybe the estimated peak time for the meal's appearance rate. Similarly,block 210 calls for storage of historical insulin-delivery informationincluding, for each instance of insulin delivery, an insulin amount anda delivery pattern. FIG. 15( a) is a graphical depiction of historicalcarbohydrate 201 and insulin delivery 203 information for a simulatedexample.

With the above information, and utilizing the Metabolic Model as withthe first embodiment, the model predictive estimator then generates, atblock 220, a predicted glucose concentration profile. In FIG. 15( b),this is shown with reference numeral 205. As described previously, inembodiments of the invention, the sensor 26 is used to obtain periodic(e.g., once every five minutes) measurements of the user's glucoseconcentration. Thus, along with the predicted glucose profile, a sensorglucose concentration profile is generated (block 230) based on theabove-mentioned periodic measurements.

Then, as called for in block 240, the predicted glucose profile 205 andthe sensor glucose profile 207 are compared, either periodically or on acontinuous basis, to determine whether, for a given point in time, thedifference between the sensor glucose concentration value and thepredicted glucose concentration value is larger than a pre-determinederror value. If the latter condition is met, then the controller and/orthe pump provides a warning to the user which may include, e.g., anaudible alarm, a visual signal, a vibrating indicator, or anycombinations thereof.

As noted above, the controller may be configured to generate a warningfor any single occurrence of an excessive amount of disparity betweenthe predicted and sensor glucose concentrations. In a variation of thisembodiment, however, historical data may be stored for the magnitude ofthe above-mentioned disparity. Then, the controller may generate awarning when each of a pre-selected number of successive comparisonsyields a disparity between the predicted and sensor profiles that isgreater than the pre-determined error value. In yet another alternativeaspect, a warning may be generated when the controller detects a trendof increasing disparities for a plurality of successive comparisons.

The glucose profiles 205, 207 may be displayed for the user, e.g., onthe pump. As shown in FIG. 15 b, glucose meter measurements 209 obtainedfrom fingerstick tests may also be displayed. Thus, for the simulationshown in FIG. 15( b), a warning may be generated at some point after 19hours, depending on the magnitude of the predetermined error valueand/or the configuration of the controller (as discussed above inconnection with alternative aspects of this embodiment). For example, awarning may be provided to the user shortly after 20 hours, at whichpoint the disparity, or discrepancy, between the predicted glucoseprofile 205 and the sensor glucose profile 207 has been growing forabout an hour.

In practice, the warning would prompt the user to check for faultycomponents, such as, e.g., a sensor, an insulin catheter, or a pumpcomponent. For example, as shown in FIG. 15( b), upon, or shortly after,receiving the warning, the user may conduct one or more fingersticktests, with meter readings 209 a which may also be displayed. Meterreadings that coincide with, or are very close to, the predicted profile205—and thus vastly different from the sensor profile 207—would thenconfirm that the sensor has failed and must be replaced. If, on theother hand, the meter readings 209 a agree with the sensor profile 207,this may be an indication that the user might have entered thecarbohydrate content of a recent meal incorrectly, or that a modelinaccuracy may be producing an erroneous predicted glucose profile. Inthese, and other similar situations, the warning provided to the userwould allow monitoring of the operation of the system, and correction ofsystem or user failures, on a real-time basis.

The third embodiment is directed to a model predictive controller (MPC)for optimizing the delivery of insulin into the user's body, so as toachieve a desired blood glucose concentration, by considering not onlyhistorical insulin-delivery data and the current state of the user'sbody, but also the future effect of insulin delivered to the user'sbody. As shown in FIG. 16, the algorithm starts at block 300 bymeasuring the user's current blood glucose concentration. At block 310,a pre-defined desired glucose trajectory is generated as the optimalprofile that starts at the current glucose state and approaches adesired steady state setpoint. In embodiments of the invention, thepre-defined glucose profile, or trajectory, may be determined pursuantto ADA guidelines for achieving a desired (goal) value of glucoseconcentration given the current glucose concentration value. Inalternative embodiments, the desired glucose trajectory may be definedin any manner that would minimize rapid fluctuations, shock to the body,etc.

The algorithm then proceeds to block 320, where an estimatedinsulin-delivery profile is generated that would attempt to achieve thedesired glucose concentration value over a predetermined time period,which may be referred to as the control horizon. Next, at block 330, themetabolic model of the MPC algorithm is used to generate a predictedglucose concentration profile over a prediction horizon based on thecurrent glucose concentration and the generated insulin-deliveryprofile. FIG. 17 shows a simulation in which the control horizon andprediction horizon are both 5 hours long, the current glucose level(i.e., 125 mg/dl) is higher than the desired glucose level (i.e., 100mg/dl), the estimated insulin delivery profile is designated byreference numeral 301, the predicted glucose concentration profile isdesignated by reference numeral 303, and the desired glucose trajectoryis shown using reference numeral 305.

Once the predicted glucose profile 303 has been generated, it iscompared to the pre-defined glucose profile. At this point in theprocess, the goal is to minimize the difference between the predictedglucose profile 303 and the pre-defined desired glucose profile 305 insuch a way as to ensure that the future estimated insulin deliveryprofile 301 is optimized. Put another way, the algorithm aims tooptimize the estimated insulin delivery profile 301 so as to generate apredicted glucose concentration profile 303 that is as close as possibleto the desired glucose concentration profile 305. To achieve this goal,embodiments of the invention may utilize an optimization process whereinan error function that describes the error, or difference, between thepredicted glucose profile 303 and the desired glucose profile 305 isminimized.

In embodiments of the invention, the optimization process may employ apenalty function in addition to the error function noted above; themathematical sum of the error function and the penalty function may bereferred to as a cost function. In practice, whereas the error functionsimply addresses the difference between the predicted and desiredglucose profiles, the penalty function provides a check against suddenchanges in insulin delivery and/or large increases in the total amountof insulin delivered. Therefore, where a cost function is employed,optimization of the estimated insulin delivery profile 301 entailsminimization of the entire cost function to not only ensure minimalerror, but also avoid sudden changes in insulin delivery/amount.

With reference to the metabolic model discussed previously, Equation 10shows one representative cost function that may be used by the modelpredictive control algorithm:

$\begin{matrix}{{\sum\limits_{i = 0}^{N_{P} - 1}\left( {{\hat{G}\left( {k + i} \right)} - {G_{D}(k)}} \right)^{2}} + {\frac{1}{\rho}{\sum\limits_{i = 0}^{N_{C} - 1}\left( {{{\hat{I}}_{D}\left( {k + i} \right)} - {{\hat{I}}_{D}\left( {k + i - 1} \right)}} \right)^{2}}}} & {{Eqn}.\mspace{14mu} (10)}\end{matrix}$

Wherein:

-   -   N_(P): Number of samples in prediction horizon    -   N_(C): Number of samples in control horizon    -   Ĝ: Estimated future glucose concentration    -   G_(D): Desired future glucose concentration    -   Î_(D): Estimated future insulin delivery rate    -   ρ: Weight that determines relative importance of achieving the        desired glucose profile versus the penalty in changing the        insulin delivery rate

In the above equation, the first half of the expression is an errorfunction expressed in terms of the sum squared difference between thepredicted glucose concentration profile and the pre-defined glucoseconcentration profile. The second half of the expression, on the otherhand, represents a penalty function that discourages sudden changes ininsulin delivery and provides a penalty for utilizing too much insulinto achieve the desired glucose profile. It is noted that, in alternativeembodiments, the error function may be defined differently than thatshown in Eqn. (10), while still accounting for the difference betweenthe predicted and desired glucose profiles. Similarly, when used, thepenalty function may be defined differently and/or may include fewer ormore terms than those reflected in Eqn. (10), depending on the number ofprocess variables that need to be controlled.

Returning to FIG. 16, and assuming the optimization process utilizes acost function, in block 340, the algorithm calculates the cost function(e.g., Eqn. (10)), and then determines whether the cost function isminimized for the proposed insulin delivery profile (block 350). If thecost function is minimized, then a command is generated for the pump todeliver insulin at a rate calculated at the current time step based onthe delivery profile which resulted in the acceptable glucoseconcentration profile (block 360). If, on the other hand, the costfunction is not minimized, then the algorithm loops back to block 320,where it generates a new insulin delivery profile, based on which a newglucose profile is generated. This process is repeated iteratively untilthe cost function is minimized at block 350. By minimizing the entirecost function, the MPC weighs the behavior of the insulin deliveryprofile in its goal to minimize the sum squared difference between the apriori predicted glucose trajectory and the desired profile.

As noted previously, once the minimal cost function has been found, acommand is generated at block 360 for the pump to deliver insulin at arate calculated at the current time step based on the (optimal) insulindelivery profile which resulted in the acceptable (predicted) glucoseconcentration profile. The insulin is then delivered for a pre-setperiod of time, or time step. In embodiments of the invention, each timestep is on the order of minutes, such as, e.g., 5 minutes. At the nexttime step, the algorithm loops back to block 300, and the process isrepeated.

While the description above refers to particular embodiments of thepresent invention, it will be understood that many modifications may bemade without departing from the spirit thereof. The accompanying claimsare intended to cover such modifications as would fall within the truescope and spirit of the present invention.

The presently disclosed embodiments are therefore to be considered inall respects as illustrative and not restrictive, the scope of theinvention being indicated by the appended claims, rather than theforegoing description, and all changes which come within the meaning andrange of equivalency of the claims are therefore intended to be embracedtherein.

1. An infusion pump for infusing insulin from a reservoir into a body ofa user, the infusion pump operating in conjunction with a glucose sensorand comprising: a housing; a drive mechanism contained within thehousing and operatively coupled to the reservoir to deliver insulin fromthe reservoir through a fluid path into the body of the user; and acontroller contained within the housing, wherein the controller monitorsthe operation of the infusion pump and glucose sensor by storinghistorical meal information for meals consumed by the user, said mealinformation including a carbohydrate content and a meal-type indicatorfor each said meal, storing historical insulin-delivery informationincluding, for each instance of insulin delivery, an insulin amount anda delivery pattern, generating a predicted glucose concentration profilebased on the historical meal and insulin-delivery information,generating a sensor glucose concentration profile based on periodicmeasurements obtained from the glucose sensor, and determining whether,for a given point in time, the difference between the sensor glucoseconcentration value and the predicted glucose concentration value islarger than a pre-determined error value.
 2. The infusion pump of claim1, wherein the controller provides a warning to the user when thedifference between the sensor glucose concentration value and thepredicted glucose concentration value is larger than the pre-determinederror value.
 3. The infusion pump of claim 1, wherein the controllerperiodically repeats the determining step and provides a warning to theuser when the difference between the sensor glucose concentration valueand the predicted glucose concentration value is larger than thepre-determined error value for a plurality of successive determinations.4. The infusion pump of claim 1, wherein, for each meal, the meal-typeindicator is the amount of time corresponding to the peak of the meal'sappearance rate.
 5. A method of monitoring the operation of adiabetes-management system having a glucose sensor, a controller, and aninsulin delivery pump, the method comprising: (a) storing historicalmeal information for meals consumed by a user, said meal informationincluding a carbohydrate content and a meal-type indicator for each saidmeal; (b) storing historical insulin-delivery information including, foreach instance of insulin delivery, an insulin amount and a deliverypattern; (c) generating a predicted glucose concentration profile by thecontroller based on the historical meal and insulin-deliveryinformation; (d) generating a sensor glucose concentration profile basedon periodic measurements obtained from the glucose sensor; and (e)determining whether, for a given point in time, the difference betweenthe sensor glucose concentration value and the predicted glucoseconcentration value is larger than a pre-determined error value.
 6. Themethod of claim 5, further including providing a warning to the userwhen the difference between the sensor glucose concentration value andthe predicted glucose concentration value is larger than thepre-determined error value.
 7. The method of claim 6, wherein thewarning is a member selected from the group consisting of an alarm, avisual signal, a vibrating indicator, and combinations thereof.
 8. Themethod of claim 5, further including displaying the predicted and sensorglucose concentration profiles for the user.
 9. The method of claim 8,wherein the profiles are displayed on the insulin delivery pump.
 10. Themethod of claim 5, wherein, for each meal, the meal-type indicator isthe amount of time corresponding to the peak of the meal's appearancerate.
 11. The method of claim 5, wherein the determination of step (e)is repeated periodically, and the method further includes providing awarning to the user when the difference between the sensor glucoseconcentration value and the predicted glucose concentration value islarger than the pre-determined error value for a plurality of successivedeterminations.
 12. The method of claim 5, wherein said delivery patternis either a single-bolus pattern or an extended-bolus pattern.
 13. Themethod of claim 5, wherein the periodic measurements obtained from theglucose sensor are transmitted directly to the controller.
 14. Themethod of claim 5, wherein the diabetes-management system is aclosed-loop system.